Operational Margin From Weather and Motion Database for Heavy Transport Vessels

2011 ◽  
Author(s):  
Jan B. de Jonge ◽  
Onno A. J. Peters
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Ship Motion ◽  
Environmental Data ◽  
Weather Data ◽  
Background Information ◽  
Motion Data ◽  
Design Values ◽  
Wave Heights

While shipping large and heavy cargo like jack-up rigs or semi-submersibles, the Motion Monitoring and Captain Decision Support system is a valuable tool to ensure a safe and economical voyage. Using the dynamic characteristics of the vessel, in combination with 5-day weather forecasts and design limits like maximum accelerations at the cargo location, roll motion and/or leg bending moments, more and better information is available to the Master to choose safe route, heading and speed. This way the best knowledge of what to expect is contributing to the safety of cargo, vessel and crew. The Octopus onboard system gathers a large amount of information about ship position, speed, heading, nowcast weather data and corresponding ship motion data. Reference is made to the paper of Peters [2] for background information of the Octopus Motion Monitoring and Decision Support system and an overview of methods used by the motion measurement system. In May 2008 the first Dockwise vessel started to gather weather and ship motion data. It is estimated that each vessel gathers around 50.000 nautical miles of data in a year, which is all collected in a database. The paper presents how this information is used for general research to environmental data, ship motion data and comparison to design values. Scatter diagrams from nowcast weather data can be produced. After collecting a certain amount of measurements, so called Dockwise scatter diagrams could be used as input for future voyage calculations. With this engineering approach Masters decisions for weather routing and bad weather avoidance is taken into account. This could lead for example to reduced design wave for a passage around the Cape of Good Hope. Now casted weather data and ship motions data is compared to design values from the cargo securing manual. Statistics like maximum difference, average difference give extensive data and insight in the operational margin of Dockwise transports. The calculation of the operational margin is independent of the standard safety margin valid for each transport. The conclusion is that the recorded nowcast significant wave height for the analyzed voyages never exceeded 5.0 [m]. With larger design wave heights the minimum operational margin increases to more than 40%, while the lowest operational margin occurs at design wave heights around 4.5 [m]. The database built by gathering all relevant information from the system and from crew observations, increases insight in the operational margins, which contributes to increased knowledge and safety.

Motion Monitoring and Decision Support During Heavy Transport

2010 ◽  
Author(s):  
Onno A. J. Peters ◽  
Leon J. M. Adegeest
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Bending Moment ◽  
Background Information ◽  
Weather Forecasts ◽  
Measurement Systems ◽  
System A ◽  
Vessel Response ◽  
Transport Vessel

During transports of large heavy cargo like jack-up rigs or semi-submersibles, the Motion Monitoring and Captain Decision Support system is a valuable tool to ensure a safe and economical voyage. Using the dynamic characteristics of the vessel in combination with 5-day weather forecasts and design limits like maximum accelerations at the cargo location, roll motion and/or leg bending moment, more and better information is available to the Master to choose a safe heading, speed and route. This way the best knowledge what to expect is contributing to the safety of cargo, transport vessel and crew. Besides use in heavy transport, this system is widely used on container ships, LNG carriers, all kinds of offshore vessels and many other types of floating structures. With daily communication, all important information is made available on internet to the operator’s main office, from which clients are informed with a comprehensive and concise overview of what is happening with their property. After the voyage, clients can be provided with the recorded Motion Monitoring data, which is valuable information for the lifetime assessment. The paper is presenting background information of the Motion Monitoring and Captain Decision Support system, a brief overview of methods used by the system and is describing the relations between transport vessel, main office and client and between the Transport Manual and the system. Results of two independent measurement systems are giving proof of high accuracy of the measurements. Comparison between measurements and predicted vessel response are shown and explained.

The Warning Decision Support System–Integrated Information

2007 ◽  
Vol 22 (3) ◽  
pp. 596-612 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Gregory Stumpf ◽  
Kurt Hondl
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Severe Weather ◽  
Weather Data ◽  
Spatial Coverage ◽  
Analysis System ◽  
Automated Algorithms ◽  
The Individual ◽  
Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

scholarly journals A decision support approach to achieve competitive advantage for a hypermarket chain

2021 ◽  
Author(s):  
Aapo Siljamäki
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Competitive Advantage ◽  
Support System ◽  
Development Process ◽  
Background Information ◽  
Economic Downturn ◽  
Observation Matrix ◽  
Shopping Basket

AbstractThis paper describes the decision support approach used in the development process of the S Group's Prisma hypermarket chain in Finland. The management was looking for a new and sustainable operating model for the rapidly growing chain, and contacted the author to consult in the process. Fierce competition forced the search for new business ideas, tools and methods that would provide a clear competitive advantage. To find new perspectives, we decided to use statistical approaches and various decision support system options, such as multi-criteria modelling. A database was available for research and analysis, including data on purchasing behavior and key performance indicators (KPI). The approach had to take into account the role and impact of customers. It was highly important to include customer behavior in the analysis using shopping basket data. Shopping basket data was central in the current paper. From these, an observation matrix was created combining shopping basket data, product data and customer background information. Using multivariate methods, customer groupings and profiles were created with the data from the observation matrix. Using the customer profile and KPI data, a multi-criteria decision support system was produced to support strategic planning. The decision support system (DSS) model was created together with a market chain operational expert and an external methodological expert. We used the VIG software package developed by Korhonen (Belg J Oper Res Stat Comput Sci 27(3):15, 1987) to solve the problem because it is easy to use and requires no prior knowledge of computers or multi-objective linear programming models. Pareto Race plays a central role in the VIG system. The chain expert easily learned how to use and work with the model. The results were immediately visible and could be used to examine alternatives and assess their appropriateness. It was decided to present five different scenarios to the hypermarket chain management. The main objective of the development process was to develop a strategy that would provide the Prisma hypermarket chain with a long-term competitive advantage. Various models were developed and used to support the strategy work by analysing and exploring the data collected, prioritising and selecting decision options. Two currently retired managers (Mönkkönen, S Group, the chain manager, Prisma chain, Interview 02.06.2021, 2021), who were involved in the development process, rated the strategy process as very successful and the modelling carried out during the process significantly supported decision-making. The immediate help of DSS modelling for decision making comes from being able to provide decision makers with reasonable, better solution options to support their decision making. The final impact of decisions could be evaluated after a longer period of time, which in the case of the Prisma development project results means several comparable financial years. Finland suffered exceptionally badly from the financial crisis and the global economic downturn in 2008–2009. The Prisma chain has survived the periods and crises described above without any loss-making years, and the whole chain has grown from 16 units in 1992 to 68 units in 2020.

scholarly journals Evaluation of decision support system for agrotechnology transfer SUBSTOR potato model (v4.5) under tropical conditions

2016 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Moleen Monita Nand ◽  
Viliamu Iese ◽  
Upendra Singh ◽  
Morgan Wairiu ◽  
Anjeela Jokhan ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Dry Weight ◽  
Growing Season ◽  
Daily Basis ◽  
Weather Data ◽  
Experimental Conditions ◽  
Plant Component ◽  
Tropical Conditions

Decision Support System for Agrotechnology Transfer (DSSAT) SUBSTOR Potato model (v4.5) was calibrated using Desiree variety. DSSAT SUBSTOR Potato model simulates on a daily basis the development and growth of potatoes using inputs such as climate, soil and crop management. The experiment was conducted in Banisogosogo, Fiji Islands, during the potato growing season of 2012. Fresh and dry weights of belowground plant component (tubers) were taken during progressive harvests. The DSSAT SUBSTOR Potato model was calibrated using experimental field data, soil and weather data of the growing season. The manual calibration steps involved recalculation of soil water content and the adjustments of genetic co-efficient to suit the temperature and daylength regime similar to the experimental conditions. Tuber dry weight was used as the main parameter to evaluate the model. The R2 values of the observed and simulated model outputs before calibration for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.52, 0.49 and 0.61 respectively. After calibration, the R2 values for tuber dry yield for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.88, 0.66 and 0.92 respectively indicating a strong positive relationship between the simulated and the observed yield.

A Decision Support System for the Off-Site Emergency Protective Actions Based on the OILs for Nuclear Emergencies

2010 ◽  
Author(s):  
YongSheng Ling
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Health Effects ◽  
Support System ◽  
Environmental Samples ◽  
Nuclear Power ◽  
Environmental Data ◽  
Protective Measures ◽  
Protective Actions ◽  
Reactor Accidents

Deterministic health effects can be prevented and the risk of stochastic health effects can be reduced by taking protective actions before or shortly after a release. These actions must be based on plant conditions and then refined subsequently based on environmental measurements. Operational intervention levels (OILs) are some calculated values (e.g., ambient dose rate or radionuclide concentration) measured by instruments or determined by laboratory analysis that correspond to a GIL or GAL. Through the use of the OILs, the environmental data are assessed primarily, which are quantities directly measured by the field instrument. Default OILs have been calculated in advance on the basis of the characteristics of severe reactor accidents. These default OILs are used to assess environmental data and take protective actions until sufficient environmental samples are taken and analyzed to provide a basis for their revision. This approach allows data to be quickly evaluated, and decisions on protective actions to be promptly made. A decision support system of the off-site emergency protective actions based on the OILs for nuclear emergencies was discussed in this paper. The system accesses the environmental data through the default OILs and the revisions of OILs. It is applied to Daya Bay Nuclear Power Plant. In the early release, according to the characteristic of the plant, the system provides the approach to calculate the default OILs based on the accident source terms described in Reactor Safety Study of USA. Also some real factors are considered, including the meteorological parameters. When sufficient environmental samples are taken and analyzed to provide a basis for their revision, the default OILs can be revised or recalculated by them. In the entire emergency planning zone, the environmental data will be assessed through the use of those OILs to provide the advice of protective measures.

scholarly journals A Customized Decision Support System for Renewable Energy Application by Housing Association

2019 ◽  
Vol 11 (16) ◽  
pp. 4377 ◽  
Author(s):  
Aleksandra Besser ◽  
Jan K. Kazak ◽  
Małgorzata Świąder ◽  
Szymon Szewrański
Keyword(s):  
Renewable Energy ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Spatial Data ◽  
Housing Association ◽  
Local Scale ◽  
Environmental Data ◽  
Housing Associations ◽  
Entire Area

One of the major problems in socio-environmental systems is the growing depletion of non-renewable resources and environmental degradation, resulting from inadequate environmental management and planning. Deepening environmental problems have forced countries to create management instruments that will help repair damage and support environmental protection efforts. The aim of this research is to develop a customized decision support system for the management of renewable energy based on the existing Geographic Information Systems (GIS). The proposed tool enables assessing the potential of solar energy production at the local scale, analyzing each rooftop. Due to the scale of the analyzed area and the details of the assessment, the tool is customized to the needs of housing associations. The system combines an existing GIS tool for calculating the solar radiation potential of rooftops (SOLIS) together with Tableau software that was used to aggregate and analyze data. In order to present the applicability of the developed tool, visualizations were prepared based on housing buildings managed by the “Biskupin” Housing Association in Wrocław (Poland) which is responsible for the management of 3415 residential premises. The created system based on spatial and environmental data will help to decide how to manage the available resources and the environment at the local scale while reducing the pressure on the environment. The tool allows for the aggregation, filtering and presentation of spatial data for the entire area of a housing association, as well as for a single building.

scholarly journals Evaluation of the BlightPro Decision Support System for Management of Potato Late Blight Using Computer Simulation and Field Validation

2015 ◽  
Vol 105 (12) ◽  
pp. 1545-1554 ◽  
Author(s):  
Ian M. Small ◽  
Laura Joseph ◽  
William E. Fry
Keyword(s):  
Computer Simulation ◽  
Decision Support ◽  
Decision Support System ◽  
Late Blight ◽  
Support System ◽  
Field Experiments ◽  
Crop Protection ◽  
Disease Suppression ◽  
Weather Data ◽  
Interactive System

The objective of this study was to evaluate the utility of the BlightPro decision support system (DSS) for late blight management using computer simulation and field tests. Three fungicide schedules were evaluated: (i) calendar-based (weekly) applications, (ii) applications according to the DSS, or (iii) no fungicide. Simulation experiments utilized 14 years of weather data from 59 locations in potato-producing states. In situations with unfavorable weather for late blight, the DSS recommended fewer fungicide applications with no loss of disease suppression; and, in situations of very favorable weather for late blight, the DSS recommended more fungicide applications but with improved disease suppression. Field evaluation was conducted in 2010, 2011, 2012, and 2013. All experiments involved at least two cultivars with different levels of resistance. DSS-guided and weekly scheduled fungicide treatments were successful at protecting against late blight in all field experiments. As expected, DSS-guided schedules were influenced by prevailing weather (observed and forecast) and host resistance and resulted in schedules that maintained or improved disease suppression and average fungicide use efficiency relative to calendar-based applications. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

scholarly journals Towards an Effective Imaging-Based Decision Support System for Skin Cancer

2022 ◽  
pp. 354-382
Author(s):  
Ricardo Vardasca ◽  
Carolina Magalhaes
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Skin Cancer ◽  
Medical Imaging ◽  
Decision Support System ◽  
Support System ◽  
Imaging Modality ◽  
Imaging Modalities ◽  
Background Information ◽  
Available Technology

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct applications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suffers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.

A Meteorological Data Service System and its Application

2011 ◽  
Vol 304 ◽  
pp. 310-315 ◽  
Author(s):  
Xin Wen Yu ◽  
Yan Chen Yang ◽  
Xu Zhang
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Service System ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Weather Data ◽  
Data Service ◽  
Agricultural Practice ◽  
Data Source

Meteorological conditions play an important role in agricultural practice and agricultural DSS usually takes weather data as a critical data source. A meteorological data service system was designed and implemented to provide better performance for Chinese users. Based on the service system, a web application providing online weather data retrieval and downloading was also developed. The service system was practically used in a decision support system for eucalypt management, and proved to be very feasible as an online weather data source for agricultural decision support system. Base on this service system, it is expected that agricultural researchers and decision support systems can easily obtain weather data and further improve their agricultural decision making process.

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